We implemented the projector network using three multi-layer perceptrons (MLP) followed by Batch Normalization [1]} and ReLU activation functions [2]}. The filter sizes were set to 64. The GNN following the stacked MLPs was also ReLU activated with a filter size of 64. The initial point of FPS is randomly selected since we did not observe any influence on the performance. We selected 15 neighbours for each cluster center selected by FPS. The filter size of the attention-based refinement layer was set to 3, mapping the (64+3) features of the selected points to \((x,y,z)\)  coordinates. We trained our model for 150 epochs with learning rate of 0.001 and a weight decay of 0.99 on every epoch using the Adam optimizer [3]}.
